According to the World Health Organization (WHO), the disease diabetes is expected to be the seventh leading cause of death by 2030. In Europe more than 52.8 million people are diagnosed with diabetes, with the number expected to rise to 64 million by 2030. In the United States, a total of 23.6 million people, that is, 7.8% of the U.S. population, have diabetes. However, only 17.9 million of those cases are diagnosed. It was found to be the fourth most frequently managed chronic disease in general practice in 2009, and the projections go as high as the second most frequent disease by the year 2030. Diabetes causes damage to the retina of patients suffering from it for over 10 years. This condition is known as diabetic retinopathy. According to WHO more than 75% of patients who have had diabetes for more than 20 years will develop some form of diabetic retinopathy.
Diabetic retinopathy is a chronic progressive and potentially sight-threatening disease of the retinal microvasculature. It is associated with the diabetes mellitus, which is one of the leading causes of diabetes-related deaths, disabilities, and economic hardship. It is the major cause of visual morbidity due to the presence of clinical abnormalities. Approximately 25,000 people go blind every year because of diabetic retinopathy. Retinal images provide useful information about the status of the eye. The retinal microvasculature is unique in that it is the only part of human circulation that can be directly and non-invasively photographed in vivo.
The presence of exudates in retinal images is one of the primary symptoms of diabetic retinopathy. Consequently, exudate detection has become a significant diagnostic task. To segment exudates, many algorithms require training on clean and filtered reference images, using manual annotation of the individual lesions, which is a tedious and time-consuming task and is prone to human errors.
Further, to optimize automated processing of retina images, the inter- and intra-image variations (e.g., light diffusion and retinal pigmentation) should be taken into account. To eliminate (minimize) such effects, pre-processing is usually required (e.g., contrast enhancement). Moreover, the appearance of exudates shows a rich variety regarding their shapes, locations, and sizes, making automatic detection more challenging.
What is needed is a novel efficient architecture and method to provide an automated detection of exudates in an ocular fundus. The method should be reliably usable to efficiently detect the exudates and be robust against inter-image and intra-image variations while requiring no classifier training associated with machine learning.